Assessing coastal marine pollution monitoring structures using a combined AHP-TOPSIS decision model

Coastal marine pollution is a major pressure on nearshore biodiversity, degrading habitats and altering food webs through nutrient enrichment, toxic contaminants, and plastic debris. These impacts can be spatially patchy and episodic, making timely detection and tracking essential for protecting ecosystem health and coastal services. Accordingly, coastal managers increasingly rely on sensor networks to detect and track marine pollution, yet alternative monitoring architectures differ markedly in performance, cost, and robustness. An integrated Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) framework is employed to evaluate three representative architectures, moored buoy system (A₁), mobile autonomous platforms (A₂) and an IoT-based underwater sensor network (A₃), against five criteria: detection capability, spatio-temporal coverage, life-cycle cost, energy/maintenance demand and operational robustness. Judgments from 100 experts were collected via an online questionnaire; 92 respondents provided complete AHP pairwise-comparison matrices, and 86 of these satisfied the AHP consistency threshold (CR ≤ 0.10) and were used to derive criteria weights. Aggregated weights indicate that detection capability (0.31) and coverage (0.24) are the dominant criteria, followed by cost (0.18), robustness (0.14), and energy/maintenance (0.13). Sensitivity analyses, including ±20% weight perturbations, cost- and performance-oriented scenarios, and 100 stochastic perturbations of performance scores, consistently retain A₃ as the top alternative. Results support IoT-based underwater sensor networks as the most balanced option for coastal pollution monitoring under the examined conditions and demonstrate the practicality of AHP-TOPSIS for transparently comparing complex marine observation architectures.